CN112861416A - Biomass fixed carbon measurement and modeling method based on near infrared spectrum principal component and neural network - Google Patents
Biomass fixed carbon measurement and modeling method based on near infrared spectrum principal component and neural network Download PDFInfo
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- 239000002028 Biomass Substances 0.000 title claims abstract description 46
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- 238000012706 support-vector machine Methods 0.000 claims description 7
- 238000000513 principal component analysis Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
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- 239000000446 fuel Substances 0.000 abstract description 7
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- 239000007787 solid Substances 0.000 abstract description 2
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Abstract
The invention discloses a biomass fixed carbon content measuring and modeling method based on near infrared spectrum main components and a neural network, which measures the biomass fixed carbon content by adopting a quantitative analysis method according to a standard (such as national standard GB/T28731 and 2012 'solid biomass fuel industry analysis method'), obtains a biomass fixed carbon content measuring value, and measures the near infrared spectrum of a biomass sample by adopting a near infrared spectrometer; measuring state parameters such as ambient temperature, ambient pressure, distance between an infrared sensor probe and a sample, ambient light intensity and the like during near-infrared data acquisition; preprocessing the obtained spectral data such as baseline drift, smooth denoising and the like; and (3) correlating the biomass near infrared spectrum and the environment-related state parameters with the fixed carbon content measurement value to construct a prediction model. The method has no damage to the biomass sample, fully considers the influence caused by the measurement environment, and can realize rapid detection and online measurement of the fixed carbon content in the biomass.
Description
Technical Field
The invention belongs to the field of biomass big data analysis, and relates to a method for quickly and accurately measuring fixed carbon in a biomass raw material.
Background
The utilization of biomass belongs to waste utilization and is not limited on one aspect, the fixed carbon of biomass fuel plays a crucial role in the operation of a boiler, a calculation method is adopted for detecting the content of the fixed carbon of the biomass, the moisture, the ash and the volatile components of the biomass fuel are measured according to a method in the national standard GB/T28731 & lt 2012 & gt industry analytical method for solid biomass fuel, and the mass of the moisture, the ash and the volatile components is subtracted from the total mass to obtain the content of the fixed carbon.
The patent CN107356551A provides a method for detecting the carbon content of rape stalks by using a near infrared spectrum, which adopts the method that the correlation relation between infrared data of 7 sections of the rape stalks and the carbon content is established and modeled, and finally, a corrected model is used for prediction; the prediction residual obtained by the method is more than 0.5 percent, and the stalks need to be processed by a plurality of cross sections, so that the processing complexity is high.
Patent CN102798607A discloses a method for estimating organic carbon content in soil by using mid-infrared spectroscopy, which establishes a correlation model by correlating infrared data of soil with real organic carbon content, and performs detection and prediction after passing through a detection sample set.
Disclosure of Invention
The biomass fixed carbon content measuring and modeling method based on the near infrared spectrum main component and the neural network provided by the invention provides a non-contact rapid measuring method which can realize online real-time measurement and fully considers the influence of the measuring environment.
To achieve the object, the invention comprises the following features:
the biomass fixed carbon content measurement and modeling based on the near infrared spectrum principal component and the neural network mainly comprises infrared spectrum measurement, biomass fixed carbon content measurement, state parameter measurement, principal component analysis and a support vector machine modeling method.
Mainly comprises the following steps:
(1) collecting biomass original data: measuring the fixed carbon content of the biomass according to a standard (such as national standard GB/T28731-2012); measuring a biological sample by using a near infrared spectrum instrument (the wavelength range is 1200-3000 nm) to obtain infrared spectrum data of the sample; measuring state parameters such as ambient temperature, ambient pressure, distance between an infrared sensor probe and a sample, ambient light intensity and the like during infrared data acquisition;
(2) dividing a sample set: selecting 30-70% of samples as a training sample set and the rest of data as a verification sample set by adopting a random classification method; the training sample set is used for training the neural network model, and the verification sample set is used for detecting whether the established neural network model is accurate or not;
(3) removing abnormal data: in the invention, a Chauvenet inspection method is adopted to process abnormal data of the data, remove the abnormal data from the data set, eliminate gross errors of the data and enhance the accuracy of the subsequent data modeling;
(4) preprocessing infrared spectrum data: the method adopts a principal component analysis method to carry out principal component analysis on the measurement data of each wavelength of the infrared data to obtain a principal component expression and a principal component numerical value; after the dimensionality reduction is carried out on the preprocessed data by using the principal components, selecting the components with the contribution rate of more than 80 percent as the principal components, removing the repeatability and the collinearity of the data, reducing the data calculation amount and ensuring the original characteristics of the data;
(5) establishing a neural network model: taking the main component data and the state parameter data of the preprocessed infrared spectrum as input parameters of a neural network of a support vector machine, taking a measured value of the fixed carbon content of the biomass as output parameters of the neural network, training the established neural network by adopting a training sample set, and finishing an optimization training process when the error is less than 0.1% to obtain an optimal network structure and parameters;
(6) verifying the accuracy of the model: and (5) adopting infrared spectrum data and state parameter data in the verification sample set as the input of the trained and converged support vector machine neural network in the step (5), and comparing the output value of the neural network with the measured value of the biomass fixed carbon in the verification sample set to verify the accuracy of the model.
The beneficial effects of the invention include: the method has the advantages that the fixed carbon measurement is carried out on the biomass fuels in different states, the types of the biomass fuels are not limited, and the method for accurately, quickly, conveniently and quickly detecting the fixed carbon is obtained by modeling the near infrared spectrum by using a PCA-SVM method.
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FIG. 1 is a flow chart of the present invention.
Detailed description of the preferred embodiments
The following detailed description of specific embodiments of the above steps, and the examples of the present invention include, but are not limited to, the present examples.
(1) Measurement of raw biomass data: selecting 100 groups of biomass samples, carrying out infrared data measurement on biomass raw materials in different states to obtain 40000 groups of infrared data, carrying out smooth denoising on the data, recording the ambient temperature and pressure, the distance between an infrared sensor and the sample, the ambient light intensity and other state parameter data during near infrared spectrum data acquisition, and measuring the content of the biomass fixed carbon according to the standard (such as the national standard GB/T28731 one-year-old 2012);
(2) dividing a sample set: carrying out random classification on 40000 groups of collected infrared data and corresponding state parameters, and dividing the data into a training sample set and a verification sample set, wherein 20000 groups are used as the data of the training sample set, 20000 groups are used as the data of the verification sample set, the training sample set is used for training a neural network model, and the verification sample set is used for detecting whether the established neural network model is accurate or not;
(3) removing abnormal data: removing abnormal data by using a Chauvenet inspection method, removing the abnormal data from the data set, and removing gross errors of the data to obtain a training sample set S with the abnormal data removed to enhance the accuracy of the subsequent data modeling;
(4) preprocessing infrared spectrum data: analyzing S as input data of principal component analysis, solving a correlation matrix C of the S, solving a characteristic value of the correlation matrix C, obtaining an accumulated contribution rate, selecting a component with the accumulated contribution rate more than 80% as a principal component to obtain a principal component matrix F, reducing the dimension of the data, removing the repeatability and the collinearity of the data, preventing the situations of excessive fitting and the like, reducing the data calculation amount and ensuring the original characteristics of the data, wherein the contribution rate is equal to the quotient of the sum of the component characteristic value and the characteristic value to obtain the accumulated contribution rate;
(5) establishing a neural network model: taking a main component matrix F of the preprocessed infrared spectrum and state parameter data as input parameters of a neural network of a support vector machine, taking a biomass fixed carbon content measurement value Y as an output parameter of the neural network, training the established neural network by adopting a training sample set, and finishing an optimization training process when an error is less than 0.1% to obtain an optimal network structure and parameters;
(6) verifying the accuracy of the model: and verifying the neural network model by using a verification set to obtain a fixed carbon calculation value Y, comparing the fixed carbon content measurement value with a predicted value Y of the neural network, and solving a correlation coefficient and a root mean square error between the predicted value and a true value to evaluate the accuracy of the rapid detection of the integral model.
The methods of the present invention include, but are not limited to, analytical measurement of biomass fuel components.
Claims (8)
1. The biomass fixed carbon content measurement and modeling method based on the near infrared spectrum main component and the neural network is characterized by comprising the following steps of:
(1) collecting biomass original data: measuring the fixed carbon content of the biomass according to a standard (such as national standard GB/T28731-2012); measuring a biological sample by using a near infrared spectrum instrument (the wavelength range is 1200-3000 nm) to obtain infrared spectrum data of the sample; measuring state parameters such as ambient temperature, ambient pressure, distance between an infrared sensor probe and a sample, ambient light intensity and the like during infrared data acquisition;
(2) dividing a sample set: selecting 30-70% of data as a training sample set and the rest of data as a verification sample set by adopting a random classification method;
(3) removing abnormal data: in the invention, a Chauvenet inspection method is adopted to process abnormal data of data, useful data signals are extracted, and signals with low noise point ratio and relatively small background interference are obtained;
(4) preprocessing infrared spectrum data: the method adopts a principal component analysis method to separate noise from background, and carries out principal component analysis on the measurement data of each wavelength of infrared data to obtain a principal component expression and a principal component numerical value;
(5) establishing a neural network model: using the preprocessed main component data of the infrared spectrum and the state parameter data as input parameters of a neural network of a support vector machine, using a measured value of the fixed carbon content of the biomass as output parameters of the neural network, and training the established neural network by adopting a training sample set to obtain an optimal network structure and parameters;
(6) verifying the accuracy of the model: and (5) adopting infrared spectrum data and state parameter data in the verification sample set as the input of the trained and converged support vector machine neural network in the step (5), and comparing the output value of the neural network with the measured value of the biomass fixed carbon in the verification sample set to verify the accuracy of the model.
2. The method for measuring and modeling the content of the biomass fixed carbon based on the near infrared spectrum main component and the neural network as claimed in claim 1, wherein: measuring a biological sample by using a near infrared spectrometer to obtain infrared spectrum data of the sample; and measuring the ambient temperature, pressure, the distance between the infrared sensor probe and the sample, ambient light intensity and other state parameters during infrared data acquisition.
3. The method for measuring and modeling the content of the biomass fixed carbon based on the near infrared spectrum main component and the neural network as claimed in claim 1, wherein: classifying the acquired data, selecting 30-70% of the data as a training sample set by adopting a random classification method, and taking the rest of the data as a verification sample set; the training sample set is used for establishing a neural network model, and the verification sample set is used for detecting whether the established neural network model is accurate or not.
4. The method for measuring and modeling the content of the biomass fixed carbon based on the near infrared spectrum main component and the neural network as claimed in claim 1, wherein: and (3) smoothing processing method, wherein a Chauvenet inspection method is adopted to remove abnormal data to enhance the accuracy of data modeling.
5. The method for measuring and modeling the content of the biomass fixed carbon based on the near infrared spectrum main component and the neural network as claimed in claim 1, wherein: and (4) carrying out principal component analysis on the measurement data of each wavelength of the infrared data, carrying out dimensionality reduction on the preprocessed data, selecting components with contribution rate more than 80% as principal components, obtaining a principal component expression and a principal component numerical value, removing repeatability and collinearity of the data, preventing the situations of overfitting and the like, reducing data calculation amount and ensuring original characteristics of the data.
6. The method for measuring and modeling the content of the biomass fixed carbon based on the near infrared spectrum main component and the neural network as claimed in claim 1, wherein: training and optimizing the neural network by using a support vector machine neural network method to training sample set data, wherein the input of the model is a principal component vector extracted after principal component analysis, the output is a fixed carbon content value in the biomass raw material, and the principal component analysis method is adopted to consider the influence of data with different wavelengths, so that data dimension reduction processing is performed, and the calculated amount is reduced; the environment state parameters of the infrared analyzer are directly used as neural network input, the influence of environment change on measurement accuracy is fully considered, the result is more accurate and reliable, and the application range is wider.
7. The method for measuring and modeling the content of the biomass fixed carbon based on the near infrared spectrum main component and the neural network as claimed in claim 1, wherein: and (6) evaluating the accuracy of the rapid detection of the whole model by using the model to calculate the correlation coefficient and the root mean square error of the training sample set and the verification sample set.
8. The application of the biomass fixed carbon content measuring and modeling method based on near infrared spectrum principal components and neural network according to any one of claims 1-7 in the fields of biomass and big data analysis belongs to the protection scope of the patent claims.
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